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After more than eight years of the European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) acquisitions, an exhaustive, empirical characterization of the biases and uncertainties affecting SMOS brightness temperatures over the ocean is possible. We show that both parameters strongly depend not only on the position in the field of view, but also on the geographical location of the acquisition. Metrics based on the differences between expected and theoretical values of the bias and the uncertainty are developed and used for quantitatively assessing the locations where SMOS errors are currently not accurately characterized. This characterization can be used for the definition of a new empirical SMOS sea surface salinity (SSS) bias correction, a better cost function retrieval, and more accurate filtering criteria, which are expected to lead to a better SMOS SSS Level 2 product. We present a new L2 SMOS SSS product based on the described investigation. The performance of this preliminary product is similar to that of the version v662 of the official L2 SMOS SSS product at medium and low latitudes. However, it provides a better coverage at high latitudes and coastal regions affected by radio frequency interference (RFI), which correspond to those regions where the SMOS errors are currently poorly estimated. [ABSTRACT FROM AUTHOR]

Since the beginning of the soil moisture and ocean salinity mission, the pervading presence of radio frequency interferences (RFI) has been one of the most problematic issues. The effect of an RFI is not just a hot spot but also six tails along the three main axes, and the general presence of ripples which degrade the quality of L1 brightness temperature snapshots. The standard mitigation technique is to apply an apodization (Blackman), but such a low-pass filter leaves traces of the tails and spreads the signal of the main lobes. New RFI mitigation techniques, such as nodal sampling, are very effective in reducing the impact of tails and ripples, but in some cases they lead to the spread of the RFI main lobe, with a significant loss of data on the affected area. In this letter, we propose a new technique to reduce their spread by an adaptive thresholding on a bandpass filtered version of the snapshot, with a significant recovery of data. [ABSTRACT FROM AUTHOR]

Estimating sea surface salinity by European Space Agency's soil moisture and ocean salinity (SMOS) mission in coastal regions is still a challenging task. The brightness temperature measured by the SMOS interferometric radiometer is biased at coastal areas by the presence of land–sea contamination and other variable sources of error, mainly human-induced radio frequency interferences (RFI). In this paper, we will discuss on the benefits for retrieving salinity maps in coastal regions of applying two correction techniques that enhance the quality of brightness temperatures: the correction of residual multiplicative errors at calibration level (for the mitigation of systematic biases close to landmasses) and the nodal sampling at imaging level (for the reduction of Gibbs-like contamination). The joint application of both techniques leads to significant improvements in salinity retrievals not only in open ocean, but also in strongly contaminated coastal regions. Comparisons of SMOS salinity observations with in situ measurements show that biases of salinity differences are lower and standard deviations are significantly reduced with respect to those obtained from the current SMOS brightness temperatures. [ABSTRACT FROM PUBLISHER]

Since its launch in 2009, the European Space Agency mission Soil Moisture and Ocean Salinity (SMOS) has provided valuable information on soil moisture, sea surface salinity data, and other geophysical variables. Due to its innovative instrument, an L-band 2-D synthetic aperture interferometric radiometer, SMOS is able to provide high-resolution L2 data (as compared with other L-band missions). However, SMOS processing is complex, giving rise to the emergence of some unexpected biases. In this paper, we have analyzed the spatial structure of two-point correlations owing to the SMOS synthetic antenna, finding that they are not negligible. Those correlations can be characterized by means of effective point spread functions (PSFs). This paper indicates that the SMOS PSF matrix can be computed in a fast way from measured data without the need for any model or auxiliary data. Furthermore, this matrix can be described in terms of a convolution kernel. The knowledge of that convolution kernel can be used to improve the quality of the SMOS image and to assess the effect of changes of processing procedures, including calibration methods. [ABSTRACT FROM PUBLISHER]

Soil moisture and ocean salinity (SMOS) brightness temperature (TB) images and calibrated visibilities are related by the so-called G-matrix. Due to the incomplete sampling at some spatial frequencies, sharp transitions in the TB scenes generate a Gibbs-like contamination ringing and spread sidelobes. In the current SMOS image reconstruction strategy, a Blackman window is applied to the Fourier components of the TBs to diminish the amplitude of artifacts such as ripples, as well as other Gibbs-like effects. In this paper, a novel image reconstruction algorithm focused on the reduction of Gibbs-like contamination in TB images is proposed. It is based on sampling the TB images at the nodal points, that is, at those points at which the oscillating interference causes the minimum distortion to the geophysical signal. Results show a significant reduction of ripples and sidelobes in strongly radio-frequency interference contaminated images. This technique has been thoroughly validated using snapshots over the ocean, by comparing TBs reconstructed in the standard way or using the nodal sampling (NS) with modeled TBs. Tests have revealed that the standard deviation of the difference between the measurement and the model is reduced around 1 K over clean and stable zones when using NS technique with respect to the SMOS image reconstruction baseline. The reduction is approximately 0.7 K when considering the global ocean. This represents a crucial improvement in TB quality, which will translate in an enhancement of the retrieved geophysical parameters, particularly the sea surface salinity. [ABSTRACT FROM PUBLISHER]

The local oscillators (LOs) of the Soil Moisture and Ocean Salinity mission payload are used to shift the operating frequency of the 72 receivers to an optimal intermediate frequency needed for the signal processing. The LO temperature variations produce phase errors in the visibility, which result in a blurring of the reconstructed brightness temperature (Tb) image. At the end of the commissioning phase, it was decided to calibrate the LO every 10 min while waiting for a more in-depth analysis. During short periods of time, the LO calibration has been performed every 2 min to assess the impact of a higher calibration rate on the quality of the data. In this paper, by means of a decimation experiment, the relative errors of 6- and 10-min calibration interval data sets are estimated using the 2 min as a reference. A noticeable systematic across- and along-track pattern of amplitude \pm0.3 K is observed for Tb differences between 10 and 2 min, whereas this is reduced between 6 and 2 min. A simulation experiment confirms that the nature of such systematic pattern is due to the visibility phase errors induced by the LO calibration rate. Such pattern is propagated into the sea surface salinity (SSS) retrievals. Overall, the SSS error increase (relative to the 2 min SSS data) is about 0.39 and 0.14 psu for the 10- and 6-min data sets, respectively. This paper shows that a LO calibration rate of at least 6 min would noticeably improve the SSS retrievals. [ABSTRACT FROM AUTHOR]